Atrial Fibrillation (AF) is a very common supraventricular tachycardia (SVT) which leads to approximately one fifth of all strokes, and is the leading risk factor for ischemic stroke. However, AF is often asymptomatic and intermittent, which typically results in appropriate diagnosis and/or treatment not occurring in a timely manner. To overcome this, many cardiac devices now monitor for AF. For example, ambulatory cardiac devices, such as Holter monitors, typically monitor for AF by obtaining an electrocardiogram (ECG) signal and measuring RR interval variability based on the ECG signal. For example, the device can compare measures of RR interval variability (or in increase compared to a baseline variability) to a variability threshold, to automatically detect AF when the variability threshold is exceeded. Implantable cardiac devices that obtain an ECG signal from subcutaneous (subQ) extracardiac electrodes typically monitor for AF in the same manner.
The reason such devices rely on measures of RR interval variability for AF monitoring, as opposed relying on measures of P-waves, is that such devices can not accurately detect P-waves due to small P-wave amplitude and relative high noise level, which leads to poor signal-to-noise ratio. In contrast, such devices can detect R-waves with good accuracy.
A problem AF monitoring based on RR interval variability is that other factors, besides AF, can result in increases in measurements of RR interval variability. This leads to high false detections of AF, which can lead to inappropriate diagnosis and/or inappropriate treatment.